Evidence map›Paper›PMID 20869902›Full record

ArticleMedical image analysis2011

A non-rigid registration method for serial lower extremity hybrid SPECT/CT imaging.

Jung W Suh, Dustin Scheinost, Donald P Dione, Lawrence W Dobrucki, Albert J Sinusas, Xenophon Papademetris

Abstract readComparative StudyValidation Study
In one paragraph

Article in Medical image analysis, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 19 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Jung W SuhDepartment of Diagnostic Radiology, Yale University, New Haven, CT, USA.
Dustin Scheinost
Donald P Dione
Lawrence W Dobrucki
Albert J Sinusas
Xenophon Papademetris
Yale University · US

Funding

YALE UNIVERSITY CLINICAL AND TRANSLATIONAL SCIENCE AWARD PROGRAMUL1RR024139 · NCRR · YALE UNIVERSITY · PI SHERWIN, ROBERT S · 2006 to 2011
$56.8M
Non-Invasive Methods for Imaging Myocardial AngiogenesisR01HL065662 · NHLBI · YALE UNIVERSITY · PI SINUSAS, ALBERT J · 2001 to 2009
$3.2M
Bioimage Suite: A structural, functional and metabolic image analysis platformR01EB006494 · NIBIB · YALE UNIVERSITY · PI PAPADEMETRIS, XENOPHON · 2006 to 2009
$1.5M
NCRR NIH HHS UL1 RR024139NCRR NIH HHS UL1RR024139NHLBI NIH HHS R01 HL065662NHLBI NIH HHS R01HL065662NIBIB NIH HHS R01 EB006494NIBIB NIH HHS R01EB006494
6 · The paper itself

Abstract

Small animal X-ray computed tomographic (microCT) imaging of the lower extremities permits evaluation of arterial growth in models of hindlimb ischemia, and when applied serially can provide quantitative information about disease progression and aid in the evaluation of therapeutic interventions. The quantification of changes in tissue perfusion and concentration of molecular markers concurrently obtained using nuclear imaging requires the ability to non-rigidly register the microCT images over time, a task made more challenging by the potentially large changes in the positions of the legs due to articulation. While non-rigid registration methods have been extensively used in the evaluation of individual organs, application in whole body imaging has been limited, primarily because the scale of possible displacements and deformations is large resulting in poor convergence of most methods. In this paper we present a new method based on the extended demons algorithm that uses a level-set representation of the body contour and skeletal structure as an input. The proposed serial registration method reflects the natural physical moving combination of mouse anatomy in which the movement of bones is the framework for body movements, and the movement of skin constrains the detailed movements of the specific segmented body regions. We applied our method to both the registration of serial microCT mouse images and the quantification of microSPECT component of the serially hybrid microCT-SPECT images demonstrating improved performance as compared to existing registration techniques.

Indexed as

AlgorithmsAnimalsLower ExtremityMiceModels, StatisticalRadiographic Image Interpretation, Computer-AssistedTomography, Emission-Computed, Single-PhotonX-Ray Microtomography

Identifiers

PMID20869902
PMCPMC2988883
OpenAlexW2089799116

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.